THE DATA-DRIVEN PATTERN FOR HEALTHY BEHAVIORS OF CAR DRIVERS BASED ON DAILY RECORDS OF TRAFFIC COUNT DATA FROM 2018 TO 2019 NEAR AIRPORTS: A FUNCTIONAL DATA ANALYSIS

Fayaz, Mohammad; Khodakarim, Alireza Abadi Soheila; Hoseini, Mohammadreza; Razzaghi, Alireza · 2020 · Crossref

DOI: 10.17654/bs017020539

archive: archived pipeline: cataloged verified

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Summary

This study investigates the temporal patterns of driving offenses and average speeds near airports to inform data-driven traffic safety policies. Motivated by the high prevalence of road traffic injuries and the specific risks associated with speeding and unsafe following distances, the authors sought to identify how time of day, day of the week, airport type (international vs. domestic), and traffic direction (to vs. from) influence driver behavior. The research focuses on two major Iranian airports: Imam Khomeini International Airport (IKA) in Tehran and Isfahan International Airport (IFN). The researchers analyzed hourly traffic count data collected from 2018 to 2019 using traffic cameras located at the entrances and exits of these airports. The dataset included vehicle counts, average speeds, and records of two specific offenses: speeding and failing to maintain a safe following distance. After cleaning the data to exclude incomplete hourly records, the study employed descriptive statistics, three-way Analysis of Variance (ANOVA), and Functional Data Analysis (FDA). FDA techniques, including functional canonical correlation and Bayesian function-on-scalar regression, were used to model the continuous hourly patterns of offense probabilities and speed, while Generalized Additive Models (GAM) assessed nonlinear effects of speed on specific offenses. The results revealed statistically significant variations in driving offenses based on hour, weekday, airport type, and direction. Functional canonical correlation showed distinct temporal peaks for offenses; for instance, traffic toward IKA peaked in the evening, whereas traffic away from IKA peaked in the late morning and evening. Bayesian regression identified the interaction between station and direction as the most significant predictor of offense probability. Speeding offenses were negligible below 90 km/h but increased significantly between 90 and 120 km/h. Conversely, failures to maintain safe distances were more prevalent at the domestic airport (IFN). Average speeds peaked around 8:00 AM, with different vehicle types exhibiting distinct behavioral patterns. The study concludes that traffic offenses near airports are not uniform but follow specific temporal and contextual patterns. These findings support the implementation of time-varying speed zones and data-driven policies tailored to specific locations and times. By leveraging traffic count data and functional data analysis, authorities can better manage high-risk behaviors, such as speeding, in critical infrastructure zones like airports, thereby enhancing road safety and reducing injury risks.

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• Empirical Findings: observational prevalence, crash risk outcomes
• Methodological Resource: dataset resource